import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :      str            =    logging.get_logger(__name__)

a  :      Union[str, Any]            =    {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class 					_lowercase    (     snake_case_			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Tuple          =    """encodec"""




    def __init__(    self       ,						lowerCamelCase__=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]       ,						lowerCamelCase__=24_000       ,						lowerCamelCase__=1       ,						lowerCamelCase__=False       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=128       ,						lowerCamelCase__=32       ,						lowerCamelCase__=1       ,						lowerCamelCase__=[8, 5, 4, 2]       ,						lowerCamelCase__="weight_norm"       ,						lowerCamelCase__=7       ,						lowerCamelCase__=7       ,						lowerCamelCase__=3       ,						lowerCamelCase__=2       ,						lowerCamelCase__=True       ,						lowerCamelCase__="reflect"       ,						lowerCamelCase__=2       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1.0       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							int       =	target_bandwidths
       lowerCAmelCase_:							str       =	sampling_rate
       lowerCAmelCase_:							Tuple       =	audio_channels
       lowerCAmelCase_:							int       =	normalize
       lowerCAmelCase_:							Optional[Any]       =	chunk_length_s
       lowerCAmelCase_:							Any       =	overlap
       lowerCAmelCase_:							List[str]       =	hidden_size
       lowerCAmelCase_:							Any       =	num_filters
       lowerCAmelCase_:							List[str]       =	num_residual_layers
       lowerCAmelCase_:							Optional[Any]       =	upsampling_ratios
       lowerCAmelCase_:							Any       =	norm_type
       lowerCAmelCase_:							Dict       =	kernel_size
       lowerCAmelCase_:							str       =	last_kernel_size
       lowerCAmelCase_:							Dict       =	residual_kernel_size
       lowerCAmelCase_:							Optional[Any]       =	dilation_growth_rate
       lowerCAmelCase_:							int       =	use_causal_conv
       lowerCAmelCase_:							str       =	pad_mode
       lowerCAmelCase_:							Dict       =	compress
       lowerCAmelCase_:							Any       =	num_lstm_layers
       lowerCAmelCase_:							str       =	trim_right_ratio
       lowerCAmelCase_:							List[Any]       =	codebook_size
       lowerCAmelCase_:							List[Any]       =	codebook_dim if codebook_dim is not None else hidden_size
       lowerCAmelCase_:							List[str]       =	use_conv_shortcut

       if self.norm_type not in ["weight_norm", "time_group_norm"]:
          raise ValueError(
              F'''self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}''' )

       super().__init__(**lowerCamelCase__ )




    @property
    def      _a    (    self ):
       if self.chunk_length_s is None:
          return None
       else:
          return int(self.chunk_length_s * self.sampling_rate )




    @property
    def      _a    (    self ):
       if self.chunk_length_s is None or self.overlap is None:
          return None
       else:
          return max(1       ,						int((1.0 - self.overlap) * self.chunk_length ) )




    @property
    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	np.prod(self.upsampling_ratios )
       return math.ceil(self.sampling_rate / hop_length )




    @property
    def      _a    (    self ):
       return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
